Courses: BioE332
Large-scale neural modeling

This course will examine the dynamics of large (>1000) networks of spiking neurons, with particular focus on how these networks achieve cognitive behaviors such as working memory, selective attention, and decision making. The course will feature lectures and labs using NEST, a software simulator, and Neurogrid, a hardware simulator developed in my lab that can simulate hundreds of thousands of spiking neurons in real time. Most of the course will be project-based, allowing you to explore your individual interests using this unique simulation device (or traditional software simulations). While there are certainly many great options for neuroscience courses, I think this one may be particularly interesting for students interested in systems neuroscience, spiking network dynamics, or large-scale computational modeling.

Catalog Description: Large-scale models link cellular properties, columnar microcircuits, recurrent connectivity, and feedback projections to experimentally studied behaviors such as selective attention and working memory. Emphasis is on exploring spike-based communication and biophysics-based computation through modeling to make experimentally testable predictions. In the first half of the course, students review and implement large-scale models in the literature. In the second half of the course, students extend these models or develop large-scale models of their own. Students work in teams of two and run models with up to a million neurons in real-time on Neurogrid—a neuromorphic simulation platform developed at Stanford that delivers supercomputer-level performance.

Modeling Project BioE332 students, Sridhar Devarajan (Neurosci) and Brian Percival (EE), demoing their modeling project on dynamic routing through neuronal coherence. [Winter 2007]

Course Details: Based on modeling projects. Accompanying lectures provide background on systems neuroscience and on modeling techniques.

Prerequisites: A course in neuronal circuits (e.g., BIO 217, NBIO 258), or systems neurobiology (e.g., PSYCH 209A, NBIO 220), or computational neuroscience (e.g., NENS 220).

Goals: Link structure to function by developing circuit-level computational models of the nervous system.

Target Audience: This course is targeted to students already exposed to systems neuroscience and computational methods wishing to learn how to build multiscale models that link neuronal biophysics to neural circuits to cognitive behavior.

BioE332—Spring 2012



Class Time: Weds & Fri 12:50-2:05pm
Location: CCSR 0247
Office Hours:
- Professor Boahen: TBA
- TA (Nick Steinmetz): TBA

Class notes:

Week 1:
Lecture: Synchrony, Synchrony II
Discussion: Modeling background
Reading: Vida et al.
Assignment: Synchrony
Extension Presentation:

Week 2:
Lecture: Working memory
Discussion: Synchrony modeling results
Reading: Laing & Chow, Funahashi et al.
Assignment: Bumps and Working Memory
Extension Presentation:

Week 3:
Lecture: Decision making
Discussion: Working memory results
Reading: Wang, Roitman & Shadlen
Assignment: Decision making

Week 4:
Lecture: Neurogrid Hardware
Lecture: Neurogrid Programming I

Week 5:
Lecture: Neurogrid Programming II
Discussion: Decision Making results
Presentation: Project proposals

Week 6: No lecture/discussion

Week 7: No lecture/discussion

Week 8:
Lecture: Project progress presentations
Discussion: Project progress presentations

Week 9: No lecture/ discussion

Week 10: No lecture/ discussion

Week 11 (Finals week):
Lecture: Final project presentations
Discussion: Final project presentations

Previous Years: